the charter schools resource academic optimism ......the charter schools resource journal editorial...
TRANSCRIPT
Editor’s Note
I am pleased to present the Spring 2016 edition of The Charter Schools Resource Journal. We have three excellent articles in this issue:
Academic optimism, organizational citizenship behaviors, and student achievement at charter schools, is authored by Mustafa Guvercin of Zenith Learning and Gary Schumacher and Michelle Peters of the University of Houston—Clear Lake.
Teacher-student interactions during instructional read alouds in the elementary classroom is authored by Kristina Rouech.
Our third outstanding article for this issue is An analysis of school climate and student growth in select Michigan charter schools by Benjamin Jankens.
Thank you to our authors for considering this journal. We strive to make this online journal relevant and important in the field of charter school policy, research, and practice. Our goal continues to publish two high-quality issues per academic year. To that end, we ask all of our readers and contributors to please keep us in mind for your charter school-related research.
We must say good-bye to one of the founders of this journal and current Associate Editor, Dr. Diane Newby. Diane has served for many years at Central Michigan University before recently taking a well-deserved retirement. Her commitment, though, continued with her service on the editorial board among many other worthwhile endeavors. Her expertise, assistance, and support is duly noted and appreciated! This is the last edition that Diane will serve on and she will be missed.
Last but not least, I am happy to introduce Stephanie Mathson as a new member of our editorial board. Stephanie is a faculty librarian at Central Michigan University and her help has been invaluable, especially in pursuing indexing opportunities for our Journal and for her help in obtaining an ISSN number. Thank you and welcome aboard, Stephanie!
Thank you to all of our readers for your interest in this journal.
Respectfully,
David E. Whale, Ed.D. Editor Spring 2016
The Charter Schools Resource Journal Editorial Board
2015-2016
Editor David Whale, Ed.D. Associate Professor Central Michigan University [email protected] Associate Editors Brenda Kallio, Ed.D. Professor University of North Dakota [email protected] Diane Newby, Ed. D. Professor Emeritus Central Michigan University [email protected] Editorial Board Mary Frances Agnello, Ph.D. Assistant Professor Texas Tech University [email protected] Carolyn Brown, M.A. 1st Grade Teacher Avondale, AZ [email protected] Jabari Paul Cain, Ph.D. Associate Professor Mercer University [email protected] Larry Corbett, Ed.D. Professor Central Michigan University [email protected]
Sharon J. Damore, Ed. D. Assistant Professor DePaul University [email protected] William Leibfritz, Ph.D. Professor Central Michigan University [email protected] Aretha Marbley, Ph.D. Associate Professor Texas Tech University [email protected] Robin Ward, Ph. D. Clinical Assistant Professor Rice University [email protected] Kathy Lynn Wood, Ph.D. Associate Dean School of Education SUNY—Buffalo State [email protected] Xiaoping Li, Ed.D. Professor Central Michigan University [email protected] Stephanie Mathson, M.L.I.S., M.A. Associate Professor Central Michigan University [email protected]
Academic Optimism, Organizational Citizenship Behaviors,
and Student Achievement at Charter Schools
by
Mustafa Guvercin Zenith Learning
Gary Schumacher
Associate Professor University of Houston-Clear Lake
Michelle Peters
University of Houston-Clear Lake
2
Academic Optimism, Organizational Citizenship Behaviors, and Student Achievement at
Charter Schools
Abstract
This study examines the dynamics between academic optimism, Organizational
Citizenship Behaviors (OCBs), and school achievement. Results indicated that the level of a
school’s academic optimism influenced their level of OCBs and school Reading and
Mathematics achievement. However, the level of a school’s OCBs was not found to influence its
school Reading and Mathematics achievement.
Keywords: academic optimism; charter schools; organizational citizenship behaviors;
regression analysis; school achievement
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Academic Optimism, Organizational Citizenship Behaviors, and Student Achievement at
Charter Schools
Introduction
Determining how school properties contribute to student learning has been one of the
greatest challenges of educational researchers. The Coleman Report (1966) posited that students’
socioeconomic status is the greatest determinant of student success in schools. The report also
suggested that school properties, such as per pupil expenditure, school facilities, or number of
books in the library, had no or at best insignificant effect on student learning. Many researchers
have looked for characteristics of schools that affect student achievement, controlling for
socioeconomic status (Edmonds, 1979; Hoy, Smith, & Sweetland, 2002; Hoy, Tarter, &
Woolfolk Hoy, 2006; Jurewicz, 2004; McGuigan, 2005; Purkey & Smith, 1983). Recently,
academic optimism and Organizational Citizenship Behaviors (OCBs) have shown promising
results in this area (Bogler & Somech, 2004; DiPaola & Neves, 2009; DiPaola, Tarter, & Hoy,
2005; Hoy et al., 2006; Kirby & DiPaola, 2011; McGuigan & Hoy, 2006; Smith & Hoy, 2007;
Wagner & DiPaola, 2011). These encouraging results were the focus of this research.
Hoy (2012) suggests that most agree that socioeconomic status (SES) is a strong
predictor of student achievement. The SES of a student is a composite variable including
common indicators, such as income, educational level, and neighborhood residential stability,
mostly referred to as the free or reduced lunch status of the student. The landmark study of
Coleman et al. (1966) concluded, “Only a small part [of student achievement] is the result of
school factors, in contrast to family background differences between communities” (p. 297). The
Coleman methodology was criticized for being improperly modeled and limited choice of school
characteristics (Aitkin & Longford, 1986; Cain & Watts, 1970; Cooley, Bond, & Mao, 1981).
4
Other researchers have confirmed the results of Coleman’s study, concluding that a student’s
SES plays almost a regulating role on his or her academic achievement (Edmonds, 1979; Hoy et
al., 2002; Hoy & Sweetland, 2001; Hoy et al., 2006; Jenks et al., 1972; Smith & Hoy, 2007;
Wagner & DiPaola, 2011). These studies suggested to sociologists, educators, and practitioners
that poverty, a social ill, should be cured in society. However, the same insight may have
contributed to the notion that school context and school level organizational attributes cannot
help students unless poverty is eliminated. Even though research supported that contribution of
school characteristics to student achievement was minimal, educators could not believe that
schools are not able to affect learning for students in poverty. Indeed, this contradicts the current
national emphasis on bringing all students, including students in poverty, up to high standards.
Researchers have spent much time examining school characteristics to see which ones
contribute to student learning. Effective school research has sought to show the importance of
school leaders and those characteristics in improving student performance. Edmonds (1979) was
the first to list characteristics of effective schools suggesting that they: (a) were led by strong
principals; (b) set high expectations for students; (c) emphasized basic skills for everyone; (d)
had a structured and an orderly environment; and (e) evaluated student performance frequently
and systematically.
Recently two constructs, academic optimism and OCBs (see Appendix), encompassing
most of the characteristics defined by Edmonds (1979), have been identified as two essential
properties of effective schools in improving student success beyond SES (Hoy et al., 2006; Kirby
& DiPaola, 2009; McGuigan & Hoy, 2006; Smith & Hoy, 2007; Wagner & DiPaola, 2011).
Therefore, studying academic optimism and OCBs and their relationships to student learning
may provide another way to improve student academic achievement.
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Hoy et al. (2006) defined academic optimism as a “collective property of school” (p. 440)
consisting of three dimensions as school properties: (a) faculty’s collective efficacy, (b) faculty
trust in students and parents, and (c) the school’s academic emphasis. The student academic
attainments, after taking into account intake differences, are positively correlated with each of
these three dimensions: (a) the faculty’s collective efficacy (Goddard, 1998, 2001, 2002;
Goddard, Hoy, & Woolfolk Hoy, 2000; Tschannen-Moran & Hoy, 2001; Tschannen-Moran,
Hoy, & Woolfolk Hoy, 1998), (b) faculty trust in students and parents (Bryk & Schneider, 2003;
Goddard, Tschannen-Moran, & Hoy, 2001; Hoy et al., 2006; Hoy & Tschannen-Moran, 1999;
Tschannen-Moran & Hoy, 1998, 2000), and (c) the school’s academic emphasis (Bryk, Lee, &
Holland, 1993; Goddard, Sweetland, & Hoy, 2000; Hoy et al., 2006).
Moreover, researchers posited that academic optimism as a unified construct has
impacted student learning at any grade configuration of public schools outweighing SES and
previous achievement (Hoy et al., 2006; Kirby & DiPaola, 2009; McGuigan & Hoy, 2006; Smith
& Hoy, 2007; Wagner & DiPaola, 2011). However, academic optimism has not been tested at
other types of schools, such as charter, private, or alternative schools. Even Wagner and DiPaola
(2011) stated the need for research to be conducted on academic optimism and its effect on
student achievement in other settings.
Relevant literature on school characteristics affecting student achievement also reveals
that OCBs in schools correlate with student achievement as well (DiPaola & Hoy, 2005a;
DiPaola et al., 2005; DiPaola & Tschannen-Moran, 2001; Wagner & DiPaola, 2011). Organ
(1988) defined OCBs as “[i]ndividual behavior that is discretionary, not directly or explicitly
recognized by the formal reward system, and that in the aggregate promotes the effective
function of the organization” (p. 4). Organizational Citizenship Behaviors in schools are
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described as “voluntary and assistive teacher behaviors above and beyond performance
expectations of their official role that "go the extra mile" to help students and colleagues
succeed” (DiPaola et al., 2005, p. 894). Volunteering for school committees, helping new
teachers in their classroom management, or providing extra tutorial classes for students in the
after-school period are all examples of OCBs in schools.
There are empirical studies showing the strong correlation between student academic
achievement and OCBs (DiPaola & Hoy, 2005a; DiPaola et al., 2005; DiPaola & Tschannen-
Moran, 2001). Wagner and DiPaola (2011) found a strong correlation between academic
optimism and OCBs at the high school level and suggested that the same correlation could be
shown in different school settings. Hoy (2012) acknowledged the promising results of the
research on the relationship between OCBs and student achievement and suggested to replicate
these studies. Understanding the effects of different constructs on student achievement could
provide great knowledge for school and district leadership to influence student and school
academic outcomes, even controlling for SES.
The enactment of the No Child Left Behind (NCLB) Act in 2001 heightened school
leaders’ concern with ensuring that all students, including those in poverty, make steady progress
towards state-determined proficiency standards (U.S. Department of Education, 2002). Since
then, schools have been concerned with addressing those gaps between the academic
achievement of students from poverty and their wealthier peers (Bergeson, 2006; Lacour &
Tissington, 2011; Rowan, Cohen, & Raudenbush, 2006; Shannon & Blysma, 2002; Snell, 2003).
Academic optimism and OCBs are two emerging properties of schools promising positive effects
on student achievement. Therefore, school leaders should consider the organizational behaviors
that affect student achievement and the relations between them in order to improve the skills of
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teachers, and schools in general, and improve the achievement of all students (Hoy, 2012).
However, almost all studies on these two characteristics of schools, academic optimism and
OCBs have been conducted in regular public school settings (Wagner & DiPaola, 2011).
Charter schools are publicly funded and privately ran public schools. They operate
according to their charter proposals and state laws. Charter schools in most states, such as Texas,
are not obliged to follow all state laws regulating public schools. In other words, they are
exempt from some of those regulations, but do have to participate in statewide testing and be a
part of a state's accountability system (Consolleti, 2011; Texas Education Agency [TEA], 2013).
For this reason, charter schools, especially college preparatory schools that focus on student
learning, are also looking for ways to improve student achievement. Improving academic
optimism and OCBs among faculty may be an answer for charter schools as well. As a result,
this study addressed the following research questions: (a) Does academic optimism influence
Organizational Citizenship Behavior?; (b) Does academic optimism influence school
achievement, controlling for SES?; and (c) Does Organizational Citizenship Behavior influence
school achievement, controlling for SES?
Methods
Participants
A purposeful sample of 10 elementary college preparatory charter schools (n = 228
teachers) operated by a single charter holder in a large urban area of southeast Texas participated
in this study. More than 80.0% of teachers were female and the majority of teachers (n = 155;
69.9%) were younger than 35 years of age. Most of the teachers were also inexperienced. More
than three quarters of the teachers (n = 160; 76.2%) reported less than five years of total teaching
experience counting the current school year as well, whereas that amount increased to 95.7%
8
(n = 198) when it came to teaching in their respective schools. Only about 14.0% (n = 29) of
teachers had been teaching in their current school more than three years and less than half of the
teachers (n = 94; 44.8%) had more than three years of experience total. Approximately 90% of
the teachers reported that they were certified in their teaching subject. Student data were also
collected from the State of Texas Assessments of Academic Readiness (STAAR) test results of
fifth grade students (n = 782). In all schools, the student body was mostly composed of African
Americans, Hispanics, or both. Socioeconomic statuses of schools varied from 27.0% to 87.0%.
Instrumentation
Academic optimism. School Academic Optimism Scale (SAOS) was utilized in this
study to determine the academic optimism level of schools. The SAOS, created by Hoy, has been
shown to be a reliable and valid instrument (Hoy, 2005; Hoy et al., 2006; McGuigan & Hoy,
2006; Smith & Hoy, 2007; Wagner & DiPaola, 2011). The SAOS is a 30-item survey comprised
of three parts: (a) sense of collective efficacy (CE); (b) faculty trust in students and parents (FT);
and, (c) the school's academic emphasis (AE). Composites of each subscale were created per
participant. The greater the composite score, the greater the academic optimism. Cronbach’s
alphas were calculated to be: .94 for the entire instrument, .85 for CE, .89 for FT, and .89 for AE.
Organizational citizenship behaviors. The citizenship behavior of each school was
measured using the Organizational Citizenship Behavior scale (DiPaola & Hoy, 2004). This 12-
item instrument measures the extent of voluntary and assistive teacher behaviors above and
beyond the performance expectations of their official role that "go the extra mile" to help
students and colleagues succeed on a 6-point scale (DiPaola & Hoy, 2005). Composites were
calculated; the greater the composite score, the greater the OCBs. Cronbach’s alpha was
calculated to be .90.
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School achievement. Student achievement was measured by each school’s mean percent
correct score on the Texas state-mandated assessment (STAAR) for reading and mathematics
tests taken in the fifth grade. Fifth grade was selected since the Student Success Initiative’s (SSI)
grade advancement requirements apply to fifth grade students who take the reading and
mathematics STARR tests (TEA, 2012) and it is a transition to middle school. The reliability of
Texas assessment was established by using two internal consistency measures: (a) Kuder-
Richardson 20 (KR-20) was used for tests with only multiple-choice items (reading = 42, math =
44) and (b) Stratified coefficient alpha was used for tests with a mixture of multiple-choice and
constructed-response items (reading = .86, mathematics = .90).
Data Collection & Analysis
The charter school principals were contacted with a phone call to discuss the purpose of
the study, the process for collecting the teacher surveys, and student achievement data. A
representative from each school e-mailed an electronic survey, using SurveyMonkey, to all
school teachers. The e-mail consisted of a survey cover letter and a uniquely coded direct
hyperlink to access the electronic survey on the SurveyMonkey website. The coding allowed the
researcher to match the responses to the schools, since teachers were embedded into schools.
Upon receiving the survey responses, all data were downloaded into Excel and uploaded to SPSS
for further analysis. Student performance data (percent correct scores in STAAR reading and
mathematics tests for fifth grade students) for each participating school were collected from the
schools and added to the SPSS database. All reverse coding was completed for all required
items in both surveys before commencing data analysis.
Given that the teachers (Level 1) in this study were nested within 10 elementary schools
(Level 2), a methodological dilemma concerning the unit of analysis was created. To address
10
this issue, initially a multilevel data analysis technique, hierarchical linear modeling (HLM), was
utilized. To justify the use of a multi-level analysis, unexplained variation in the outcome or
dependent variables (OCBs and student achievement) were examined across each campus. To
do this, a one-way Analysis of Variance (ANOVA) with random effects model (unconditional
model) was used. The one-way ANOVA model contained only an outcome variable and no
Level 1 or Level 2 predictors. Given that unexplained variation was not found to exist (p > .05)
across the 10 campuses for either outcome variable, a single level analysis (simple linear and
hierarchical multiple regression) was employed to analyze the data. All variables (academic
optimism, Organizational Citizenship Behaviors, and school achievement) were of continuous
measurement.
Findings
Academic Optimism and Organizational Citizenship Behaviors
When examining whether or not teachers’ perceptions of their school’s academic
optimism influenced their perceptions of their school’s OCBs, simple linear regression
techniques were used. Findings indicated that a school’s academic optimism does have an
influence on the school’s OCBs, F(1, 226) = 191.2, adjusted-r2 = .458, p < .001. The greater the
academic optimism of a school the greater the level of that school’s OCBs. Approximately 46%
of the schools’ level of OCBs can be attributed to their level of academic optimism.
In addition, the properties of academic optimism were also found to influence OCBs.
Academic optimism consists of three properties – collective efficacy (CE), faculty trust in clients
(FT), and academic emphasis (AE). Statistically significant findings were found to exist
between: (a) CE and OCBs, F(1, 226) = 153.72, p < .001, adjusted-r2 = .405, (b) FT and OCBs,
F(1, 226) = 123.03, p < .001, adjusted-r2 = .369, and (c) AE and OCBs, F(1, 226) = 122.82,
11
p < .001, adjusted-r2 = .352. The three properties of academic optimism explained the variation
in the level of a school’s OCBs by 40.5%, 36.9%, and 35.2% respectively. Table 1 depicts the
summary of these findings.
Table 1
Academic Optimism and Organizational Citizenship Behaviors
N F-value r adjusted-r2 p-value
AO – OCB 228 191.17 .677 .458 <.001*
CE – OCB 228 153.72 .636 .405 <.001*
FT – OCB 228 123.03 .607 .369 <.001*
AE – OCB 228 122.82 .593 .352 <.001*
*Statistically significant (p < .05)
Academic Optimism and School Achievement
Hierarchical multiple regression techniques were used to examine whether or not the
teachers’ perceptions of their respective school’s academic optimism influenced their school’s
average reading and mathematics achievement, controlling for school SES.
Reading achievement. The results of the hierarchical multiple regression analysis found
statistically significant findings to exist between: (a) CE and school reading achievement,
F(1, 225) = 15.49, p < .001, adjusted-r2 = .019; (b) FT and school reading achievement,
F(1, 225) = 21.30, p < .001, adjusted-r2 = .026; (c) AE and school reading achievement,
F(1, 225) = 17.50, p < .001, adjusted-r2 = .022; and (d) AO and school reading achievement,
F(1, 225) = 22.31, p < .001, adjusted-r2 = .027, when controlling for school SES. As a school’s
academic optimism increases, so does its reading achievement. After controlling for school SES,
academic optimism, and its three properties, collective efficacy, faculty trust in clients, and
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academic emphasis, explained the variation in reading achievement by 2.7%, 1.9%, 2.6%, and
2.2%, respectively. Table 2 displays these findings.
Table 2
Academic Optimism and Reading Achievement
N F-value adjusted-r2 p-value
AO - Reading 228 22.31 .027 <.001*
CE - Reading 228 15.49 .019 <.001*
FT - Reading 228 21.30 .026 <.001*
AE - Reading 228 17.50 .022 <.001*
*Statistically significant (p < .05)
Mathematics achievement. The results of the hierarchical multiple regression analysis
found statistically significant findings to exist between: (a) AO and school mathematics
achievement, F(1, 225) = 5.70, p = .02, adjusted-r2 = .010, (b) FT and school mathematics
achievement, F(1, 225) = 4.78, p = .03, adjusted-r2 = .009, and (c) AE and school mathematics
achievement, F(1, 225) = 5.94, p = .016, adjusted-r2 = .011, when controlling for school SES.
After controlling for school SES, academic optimism and two of its properties, faculty trust in
clients and academic emphasis, explained the variation in average school mathematics
achievement by 1.0%, 0.9%, and 1.1%, respectively. Findings, however, did not conclude that
the collective efficacy of the school had any influence on the school’s average mathematics
achievement, F(1, 225) = 3.62, p = .058. The findings are displayed in Table 3.
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Table 3
Academic Optimism and Mathematics Achievement
N F-value adjusted-r2 p-value
AO - Math 228 5.70 .010 .018*
CE - Math 228 3.62 .007 .058*
FT - Math 228 4.78 .009 .030*
AE - Math 228 5.94 .011 .016*
*Statistically significant (p < .05)
Organizational Citizenship Behaviors and School Achievement
In addition, it was imperative to determine whether or not the schools’ level of OCBs had
any influence on their average school achievement, after controlling for school SES. The
findings of the hierarchical multiple regression analysis suggested that a school’s level of OCBs
does not influence the average school reading achievement, F(1, 225) = .277, p = .599, or
mathematics achievement, F(1, 225) = .039, p = .843, after controlling for school SES.
Therefore, insufficient evidence was found to support the rejection of the null hypothesis.
Discussion
Since the enactment of NCLB, schools have been even more concerned with addressing
the gap between the academic achievement of students from poverty and their wealthier peers
(Bergeson, 2006; Lacour & Tissington, 2011; Rowan et al., 2006; Shannon & Blysma, 2002;
Snell, 2003). Academic optimism and OCBs, as school properties, have shown positive effects
on student/school achievement in regular public schools (Hoy et al., 2006; Kirby & DiPaola,
2009; McGuigan & Hoy, 2006; Smith & Hoy, 2007; Wagner & DiPaola, 2011). However, it
14
was unknown if the same results could also be found in charter school settings. Therefore, the
purpose of this study was to examine the relationship among academic optimism, OCBs, and
school achievement in college preparatory charter schools.
Similar to previous research (Bryk & Schneider, 2003; Goddard et al., 2001; Hoy et al.,
2006; Kirby & DiPaola, 2009; McGuigan & Hoy, 2006; Smith & Hoy, 2007; Wagner &
DiPaola, 2011), the results of the regression analysis for this study indicated that a statistically
significant relationship existed between: (a) academic optimism and OCBs; (b) academic
optimism and school achievement, both in reading and mathematics; (c) collective efficacy and
school achievement in reading; (d) faculty trust in clients and school achievement, both in
reading and mathematics; and, (e) academic emphasis and school achievement in both reading
and mathematics.
However, unlike previous research (Bogler & Somech, 2004, 2005; DiPaola et al., 2005;
DiPaola & Neves, 2009; DiPaola & Tschannen-Moran, 2001; Goddard, 2002; Goddard et al.,
2000; Jurewicz, 2004; Somech & Ifat, 2007; Tschannen-Moran & Hoy, 2001; Wagner &
DiPaola) the results failed to find a statistically significant relationship to exist between: (a)
collective efficacy and school achievement in mathematics and (b) OCBs and school
achievement in both reading and mathematics. One reason for this could be that expectations of
teachers in charter schools are higher than those of teachers in regular public schools. Teachers
in charter schools may have different perceptions of OCBs. For example, providing extra
tutorials to students or serving on school committees or at functions are among the formal duties
of teachers in those sampled charter schools, whereas, these are among the OCBs in regular
public schools. The years of experience teachers had may have been another way to explain this
contradiction with findings from previous research. More than 50% of all teachers in the sampled
15
schools have three years or less experience. Therefore, they may still be more idealistic in their
teaching and weren’t able to distinguish their regular duties from the OCBs.
Implications
The implications of this quantitative study are not limited to only the college preparatory
charter schools that participated in this research. Other charter and public schools may benefit
from these findings as well. Findings from this research can be used by school administrators
and teachers to improve student achievement. Charter schools, like traditional public schools, do
not have control over the SES of their students. However, there are school properties that they
can control to improve school achievement. The findings of this research suggest that academic
optimism and its three properties influence student achievement, after controlling for SES, in
charter schools. Therefore, principals may work on boosting the faculty’s collective efficacy,
teachers’ trust in clients, and pressing for academic excellence. All of these components will
increase the academic optimism of a school. According to current and previous research
findings, student achievement will improve accordingly (Goddard, 2002; Hoy et al., 2006;
McGuigan & Hoy, 2006; Smith & Hoy, 2007; Wagner & DiPaola, 2011).
Additionally, findings from this research support the relationship between academic
optimism and school achievement, when SES is controlled. Schools or school districts, including
charter schools, may want to consider administering the School Academic Optimism Scale
(SAOS) to their faculty to determine deficient areas in collective efficacy, faculty trust in clients,
and academic emphasis. Administrators may plan workshops and trainings according to the
results of the survey to improve deficient areas so that high academic optimism can be
established in their schools and as a result, school achievement can be expected to increase.
16
Despite the findings, which failed to find a correlation between OCBs and school
achievement for these sampled schools, there is numerous research supporting their relationships
(Bogler & Somech, 2004, 2005; DiPaola et al., 2005; DiPaola & Hoy, 2005a, 2005b; DiPaola &
Neves, 2009; DiPaola & Tschannen-Moran, 2001; Jurewicz, 2004; Somech & Ifat, 2007;
Wagner & DiPaola, 2011). Therefore, school administrators may want to consider looking for
ways to improve OCBs in schools as well. Charter schools may consider redefining the OCBs for
their campuses since each charter has different practices in defining roles and expectations of
teachers. While one school defined tutoring in after school hours as regular duty of a teacher,
another one may not but it may require its teachers to sponsor an after school club as a regular
duty. This might be one of the reasons the current study couldn’t find a relationship between
school achievement and OCBs. The findings are valuable to the research on academic optimism,
OCBs, and charter schools. Given that this is one of the first known studies examining
correlations of academic optimism, OCBs, and school achievement in charter schools, it initiates
a discussion which examines the relationships among those constructs in charter schools.
Recommendations for Future Research
This study should be replicated including qualitative data to allow researchers to explore
the perceptions of the participants on academic optimism, collective efficacy, faculty trust, and
academic emphasis in their respective schools. It would also be beneficial to repeat this study at
the classroom level across the schools so that research might check the possible unexplained
variance in student achievement. Embedding students in classrooms and examining achievement
at the student level rather than at the school level would allow using HLM statistical method to
account for this variance. Given that the multiple regression analysis did not find OCBs to have a
direct relationship with schools’ reading and mathematics achievement, but did report that OCBs
17
had a strong relationship with academic optimism and that academic optimism had a statistically
significant relationship with school achievement, this would suggest one examine whether or not
academic optimism could potentially be a mediator between OCBs and school achievement.
Therefore, there is a need for study to examine why there was no relationship between OCBs and
school achievement or whether it was mediated through another construct.
18
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24
Appendix
Glossary of Key Terms
1. Academic Optimism (AO) - A collective property of schools, having three properties: (a)
faculty’s collective efficacy, (b) faculty trust in students and parents, and (c) the school’s
academic emphasis (Hoy, Tarter, & Woolfolk Hoy, 2006).
• Collective efficacy - The perceived collective efficacy of a school refers to the judgment
of the teachers that the faculty as a whole can organize and execute the actions required
to have positive effects on students. The school faculty believes it can teach even the
most difficult students.
• Faculty trust - The school faculty trusts students and parents.
• Academic emphasis - The academic emphasis of a school is the extent to which the
school focuses on student academic achievement. In such schools, the faculty sets high
but attainable goals for students and students work hard, are cooperative, and respect
others who get high grades.
2. Organizational Citizenship Behaviors (OCB) – The “voluntary and assistive teacher
behaviors above and beyond performance expectations of their official role that ‘go the extra
mile’ to help students and colleagues succeed” (DiPaola, Tarter, & Hoy, 2005, p. 894).
TEACHER-STUDENT INTERACTIONS DURING INSTRUCTIONAL READ-ALOUDS IN
THE ELEMENTARY CLASSROOM
By
Kristina Rouech
Central Michigan University
READ-ALOUD INTERACTIONS
2
TEACHER-STUDENT INTERACTIONS DURING INSTRUCTIONAL READ-ALOUDS IN
THE ELEMENTARY CLASSROOM
TEACHER-STUDENT INTERACTIONS DURING INSTRUCTIONAL READ-ALOUDS IN
THE ELEMENTARY CLASSROOM
ABSTRACT
Reading aloud to elementary students for instructional purposes is common practice and teachers
often ask questions while reading to engage students in conversations. This research adds to the
literature pertaining to the types of questions teachers ask while reading aloud. Six elementary
teachers and their students were observed and video recorded to examine questions teachers
asked and responses students gave during classroom read-aloud sessions. ELAN (Max Planck,
2012), video annotation software, was used for data analysis. Findings revealed no apparent
relationship between the level of questions teachers posed and the amount of talk time allocated
to students. Frequently, questions were posed that limited student response to a single word
answer. This research highlights the importance of teachers formulating effective questions prior
to the classroom read-aloud for instructional purposes.
READ-ALOUD INTERACTIONS
3
Introduction
Reading aloud for instructional purposes has been widely studied and implemented at
various grade levels; however, much of the research has been done at the lower elementary level.
As a teacher, I read-aloud to my fourth and fifth grade students on a daily basis for instructional
purposes. But some sessions left my students and I unsatisfied with the conversation and
response to the text. This led me to researching how teachers in 2nd – 5th grades read-aloud and
used trade books to best meet the educational objectives for their students.
Reading aloud for instructional purposes can engage students in quality conversations
about what they are listening to and lead to deeper understanding about content and about how
texts function. This point was made clear by Anderson, Hiebert, Scott, and Wilkerson (1985)
when they stated “The single most important activity for building the knowledge required for
eventual success in reading is reading aloud to children” (p. 23). Even though I understood what
made a good instructional read-aloud, I still had concerns about why some of them were not as
effective as others. In order to ascertain whether other teachers were having the same difficulties
as myself, I conducted research of six upper elementary teachers who read-aloud for instructional
purposes. The purpose of this study was to examine the actual talk that occurs between teacher
and students during read-aloud interactions. The following research questions guided this
qualitative study:
1. What types of questions do teachers ask when reading aloud?
2. What types of responses do students give to questions posed by the teacher when reading
aloud?
3. Is there a relationship between the types of questions teachers ask and the amount of talk
time that is allocated to students during the read-aloud experience?
READ-ALOUD INTERACTIONS
4
In order to capture authentic data all teachers were video recorded and all questions and
responses were identified using video annotation software, ELAN (Max Planck, 2012).
Using video is a relatively new form of data collection in educational research. As Loehr
and Harper (2003) noted, “the advent of two technologies – inexpensive video recorders and
digital video annotation software – is revolutionizing the study of human interaction” (p. 225).
Researchers are no longer limited by the “cumbersome mechanical manipulation of video
frames, and separation of video footage from annotation” (p. 226) due to the advancement in
video annotation software, such as ELAN (Max Planck, 2012). Currently, the use and analysis
of video data using annotation software provide educational researchers with richer data and
more complete analysis of that data.
As a result of using video annotation software and examining the three research questions
above, what became apparent is that planning prior to the read-aloud is necessary. Preparedness
prior to instructional read-aloud can increase student engagement and allow students to show
their thinking about the text rather than all knowledge coming from the teacher. Teachers need to
make the best use of the instructional read-aloud by preplanning questions for effective and
meaningful learning.
Review of the Literature
Reading aloud to students for instructional purposes is not something that can be done
well without knowledge of effective practice. Previous research clearly identified important
components of classroom read-alouds: previewing text, building background knowledge, making
predictions, building vocabulary, asking questions, reading a variety of texts, and engaging
students with different styles of interactions (Bradbury, 2006; Helsey & Kucan, 2010; Lohfink,
READ-ALOUD INTERACTIONS
5
2012; Morrow & Britain, 2003; Teale, 2003). In addition to these components, the previous
research also identified benefits of reading aloud, such as: constructing meaning from text,
gaining experience with language, instilling a joy of reading, motivating students to read,
learning new vocabulary, adding to background knowledge, teaching text structure, and engaging
students in quality talk (Barrentine, 1996; Beck & McKeown, 2001; McKeown & Beck, 2003;
Morrow & Britain, 2003; Teale, 2003). However, researchers have reported that teachers
approached read-aloud as a teacher-directed experience rather than truly engaging students in
conversation about text. As such, students lost out on valuable opportunities to explore the text
and achieve the previously mentioned benefits (Barrentine, 1996; Beck & McKeown, 2001;
Martinez & Teale, 1993).
Much of the work done in the previous research regarding read-alouds was qualitative in
nature. Researchers have collected data on effective components of read-alouds (Fisher, et al.,
2004), benefits of reading aloud (Dickinson & Smith, 1994; Kindle, 2009), and teacher styles
while reading aloud (Beck & McKeown, 2001; Hall & Williams, 2010; Martinez & Teale, 1989,
1993; Wright, 2011). In addition, many of these studies advocate for styles of read-alouds that
are interactive and suggest that teachers ask higher-level questions when reading aloud
(Barrentine, 1996; Lohfink, 2012; McGee & Parra, 2015; Beck & McKeown, 2001; McKeown
& Beck, 2003). What previous studies neglected to report was specifically what happened
between the teacher and students during the interactions of a read-aloud experience. This
qualitative study attempted to fill that gap.
Methodology
READ-ALOUD INTERACTIONS
6
This study was qualitative by design and employed a grounded theory approach to
develop codes for identifying the types of questions teachers asked and the types of responses
students gave to those questions. The data were further analyzed by examining the relationship
between the types of questions teachers asked and the amount of talk time allocated to students
during the instructional read-aloud experience. Data were collected by video recording and
observer notes, and then analyses were completed using a constant-comparison method. Codes
for question and response types were developed as the data were analyzed.
Stages for constant comparison in order to generate codes for the data were done using
the methodology of grounded theory analysis as described by Creswell (2007) and named by
McEneaney, Gillette, Farkas, Clifton, and Guzniczak (2012). The first four-steps of their process
was used in order to describe the data. The first step, “Observing,” involved viewing each video
in its entirety without attempting to code. The second step, “Describing,” involved viewing each
video and annotating potential codes from when the teacher was talking and when the students
were talking. The third step, “Generalizing,” the codes were narrowed, defined more clearly,
and annotated within the video stream. Lastly, the fourth step, “Refining,” started with
reviewing all codes and refining the content and description of said codes. At this point,
calculations of inter-rater reliability were done with a subset of three cases. These four steps
were in regard to the creation of codes for teacher questions and student responses, other steps
did take place in order to input field notes and calculate talk time.
Study Setting and Participants
A convenience sample of teachers was selected from schools located in a general
geographic location of a small Midwestern city. Public, private, and parochial schools were
READ-ALOUD INTERACTIONS
7
considered equally and the researcher recruited through the principals of all elementary schools
in the selected geographic area. Six classrooms were randomly selected from those who
volunteered to be a part of the study. All names are pseudonyms. Two of the classrooms were
in parochial settings and the remaining four were public schools. Of the 145 students in the six
classrooms, 124 had parental permission to participate in the study. Those without permission
were placed out of camera range and no data were used from those students. All six teachers
were Caucasian and ranged in age from 34 to 56 years old. Four out of the six teachers held a
master’s degree in education and their experience ranged from 11 to 17 years teaching
elementary school. All of the teachers already used read-alouds for instructional purposes in
their classrooms. Data was collected in the spring of the school year, therefore all students were
accustomed to read-alouds in their classroom.
Data Collection and Analyses
Six second through fifth grade classrooms were observed in the spring of 2013 and three
instructional read-alouds were videotaped for each classroom. The day before and after any
school break or holiday was avoided. Scheduling of data collection was done with the classroom
schedule in mind. The observations were conducted during regularly scheduled read-aloud times
in order to create minimum disruption of the classroom schedule. There were three recordings
done in each classroom in order to ensure that if the equipment failed during one read-aloud
there were still two others to use. The first recording was completed to acclimate the classroom
to the cameras and researcher and the second and third read-alouds were used for analysis.
Types of Data. In order to collect comprehensive information regarding the specific
questions of this study, the researcher gathered three particular types of data. Each teacher
completed a pre- and post-observation survey, the researcher collected field notes during each
READ-ALOUD INTERACTIONS
8
read-aloud, and each experience was recorded using two video cameras: one that focused on the
teacher and one that focused on the students. Students without permission to participate in the
study were out of camera range and their comments were blocked out of all recordings.
Survey. Prior to the first instructional read-aloud, a pre-observation survey focusing on
teacher demographic information and general experience, knowledge and understanding about
classroom read-alouds was distributed and collected. After each read-aloud, a post observation
survey was administered and focused on the teachers’ selection of text, pre-reading preparation
(if any), and teacher reflection. Data collected through the pre-observation survey were used to
compile demographic data regarding each teacher in order to provide background information
about their education and experience. The post-observation survey asked about the particular
read-aloud recorded that day.
Field notes. This researcher’s role was that of an outsider, rather than a participant of the
read-aloud, in order to observe the experience as it naturally occurred in the classroom. The
presence of an outsider always carries the risk of affecting the natural environment; however, the
benefits of written field notes during the read-aloud outweighed the risk of adverse consequences
due to researcher observation. Field notes were a necessity to capture small nuances and the
overall atmosphere of the read-aloud that were lacking in the video recordings. Being present
during the read-alouds allowed the researcher to perceive the experience as a whole rather than
just watching the limited view through each recorded video stream. Field notes were integrated
into the video stream on ELAN (Max Planck, 2012) to provide a complete picture of the read-
aloud and so the information was readily available when coding. Field notes were not analyzed
separately.
READ-ALOUD INTERACTIONS
9
Video. Data of the read-aloud were gathered using video recorders. Video recordings
were done once a week for three consecutive weeks attempting to hit a variety of days and times
while making sure that the recording was done during normal read-aloud time. Two cameras
were used: one focused on the teacher and one focused on the students with permission to
participate. This created two distinct video streams offering simultaneous views focused on both
groups of participants.
Figure 1. ELAN Screenshot
Figure 1 shows the view of a video and annotations in ELAN (Max Planck, 2012) from
this study. In the upper left hand corner, viewers can observe the view of the camera focused on
the teacher. Just to the right of that is the second video, which was focused on the students. The
upper right hand corner has multiple options for viewing and creating sections for annotation.
The bottom half of the figure shows the tiers that were created in ELAN (Max Planck, 2012) for
annotating the data. Multiple tiers were created for each type of coding and then frequency
charts were compiled to display the data in tables.
READ-ALOUD INTERACTIONS
10
Reliability
The presence of the researcher in the room was the first measure to ensure reliability of
the data collected. Field notes were recorded during each read-aloud in order to make notes
about the general environment and the experience as a whole. These field notes were used to
create a content log in the notes of the video recording in order to ensure the experience was
recorded accurately. The collection of data using video recordings was also a measure to ensure
reliability. When conducting observations, a researcher has to rely solely on notes and memory
of the experience. Video recordings and annotations in ELAN (Max Planck, 2012) allow the
researcher to return to the raw data to address any concerns that may occur with interpretation of
the data.
Data were collected from multiple classrooms to increase the chance of valid
conclusions. There were also three read-alouds recorded in each classroom, of which two were
analyzed. This reduced the likelihood of data being skewed based on an extraordinary event in
that classroom or judgments being made based on isolated events. As data were collected and
analyzed, the recordings were viewed multiple times to ensure accuracy of coding, not only by
the researcher, but also by two trained colleagues.
In an effort to ensure reliability, inter-rater reliability was computed using two trained
colleagues. The researcher met with two fellow doctoral students to define and explain the codes
that were used for teacher questions and student responses. A sheet was provided showing the
codes with definitions and examples. Both colleagues had previously worked with ELAN (Max
Planck, 2012), so a brief review of the software and a reminder of how to operate the software
was all that was needed. They were given four sections of video from four separate events. Two
sessions of teachers reading picture books and two novels were selected. Two of the read-alouds
READ-ALOUD INTERACTIONS
11
were coded in their entirety, one of the beginning and middle, and one of the middle and end.
This allowed the two raters to see a variety of text and all parts of a read-aloud (beginning,
middle, and end). The only tiers that were showing were the ones showing the text of the teacher
question and student responses, as well as a tier for their coding. When each person was done
coding the four sections, this researcher put the “Teacher Question” tier back onto the screen and
created an additional tier to identify hits and misses. The data were then put into SPSS to
calculate Kappa. Cohen’s Kappa (1960) was used because it is a reliability metric that accounts
for chance and therefore provides a more rigorous measure than simple percentages.
Types of Questions Asked
In order to answer the first research question, codes needed to be identified for
categorizing the questions asked. The final codes identified in Table 1 are the result of using
constant-comparison with the raw data to determine the types of questions teachers asked while
reading aloud.
READ-ALOUD INTERACTIONS
12
The following six codes were used for identifying teacher question types and compiling the
frequency chart.
1. Literal questions refer “to an understanding of the straightforward meaning of the text,
such as facts, vocabulary, dates, times, and locations. Questions of literal comprehension
can be answered directly and explicitly from the text” (Day & Park, 2005, p. 62).
2. Vocabulary questions were direct questions asking for the meaning of word, concept, or
phrase related to the read-aloud.
3. Prediction questions “involve students using both their understanding of the passage and
their own knowledge of the topic and related matters in a systematic fashion to determine
what might happen next or after a story ends” (Day & Park, 2005, p. 63). Prediction
questions helped students to use what they previously read in order to draw conclusions
about the possibilities for the next part of the text.
READ-ALOUD INTERACTIONS
13
4. Personal response questions “require readers to respond with their feelings for the text
and the subject” (Day & Park, 2005, p. 64). These questions seemed to help students
respond to the text in a personal way and apply situations in the text to their own lives.
Dochy, Segers, and Buehl (1999) define prior knowledge as "the whole of a person's
actual knowledge that: (a) is available before a certain learning task, (b) is structured in
schemata, (c) is declarative and procedural, (d) is partly explicit and partly tacit, (e) and is
dynamic in nature and stored in the knowledge base" (p. 146).
5. Prior knowledge questions were used as a way to remind students of what previously
happened in the read-aloud or asked students to recall personal experiences that happened
prior to the current read-aloud. These types of questions allowed students to consider
what they learned or experienced previously in order to prepare and connect to the text
being read. An inference question “involves students combining their literal
understanding of the text with their own knowledge and intuitions” (Day & Park, 2005, p.
63).
6. Inference questions also included those for which the teacher’s intent seemed to be
inferential. However, in some cases, the question was worded in such a way that the
teacher helped the students to put the information together, rather than the student doing
the thinking. For example, “So, is he making, is he saying something to be nice or to not
be nice?” (Davis, event three). By including the choices of nice or not nice in her
question, Mrs. Davis limited the response of the students to “yes” or “no,” which limited
their thinking and closed off the question. The teacher had no idea if the student is
guessing, agreeing because a friend does, or even paying attention to the text at all. Just
the beginning, “so, is he” already limits the possibilities for responding.
READ-ALOUD INTERACTIONS
14
Findings
The first research question addressed the types of questions that teachers asked during the
instructional read-aloud experience. The results are displayed in Table 1. As the data were
being analyzed, it became evident that teachers were asking questions in such a way that student
response was limited. This connected to the work of Hargreaves (1984) that suggested using
codes of “open” and “closed” for teacher questions. In addition to coding questions as initially
specified, another reading/viewing of the video—as prescribed by the constant comparison
methodology—led to classifying each of the 275 questions as either open or closed. Open
questions were those “where several possible and equally valid answers are available to pupils”
(Hargreaves, 1984, p. 47). Students were able to respond with their own thinking and there were
a variety of options available for a response. For example, “What was the matter with Al?”
(Williams, event two) and “So, how was the crowd feeling?” (Davis, event two). Usually, one
student was asked to respond to these types of questions, and then the teacher moved on to the
next piece in the text. There were a total of 87 open questions across all read-alouds (Table 2).
Table 2 Total of Open and Closed Questions by Type
Infe
renc
e - O
pen
Infe
renc
e - C
lose
d
Lite
ral -
Ope
n
Lite
ral -
Clo
sed
Prio
r Kno
wle
dge
- O
pen
Prio
r Kno
wle
dge
- C
lose
d Pe
rson
al R
espo
nse
- Ope
n Pe
rson
al R
espo
nse
- Clo
sed
Pred
ictio
n - O
pen
Pred
ictio
n - C
lose
d
Voc
abul
ary
- Ope
n V
ocab
ular
y -
Clo
sed
Tota
l - O
pen
Tota
l - C
lose
d
Brown 4 5 2 0 2 3 0 0 2 4 0 0 10 12 Davis 16 14 0 7 7 18 6 12 2 6 2 2 33 59 Johnson 3 8 3 5 1 13 0 0 0 0 0 0 7 26 Jones 7 22 0 12 5 27 1 9 0 1 3 3 16 74 Smith 0 3 4 10 0 4 0 0 0 1 1 0 5 18
READ-ALOUD INTERACTIONS
15
Williams 0 2 2 1 1 2 2 2 0 0 3 0 8 7 Totals 30 54 11 35 16 67 9 23 4 12 9 5 79 196
In contrast, 196 of the questions were closed in nature. Closed questions were those in
which “there is only one correct answer acceptable to the teacher” (Hargreaves, 1984, p. 46). In
other words, these types of questions could be answered simply with yes, no, or one word
replies. Additionally, questions were classified as closed if they were worded in such a way that
they implied the answer the teacher was looking for by leading the students to the answer in
some way. For example, “Would you say Fern was seeing animals equal to people?” (Johnson,
event two) and “Is that a nice looking person?” (Jones, event three).
The second research question was focused on the types of responses students gave to
teacher-questions posed during the read-aloud. These data were also coded in ELAN (Max
Planck, 2012), then compiled into a frequency table as shown in Table 3.
READ-ALOUD INTERACTIONS
16
The coding categories identified in research question one were also used for research question
two. This researcher selected to use the same codes in order to identify if students were
answering questions in the manner the teacher asked them, because theoretically, teachers are
going to get back what they request. By using the same codes, this researcher is providing a
parallel system to examine if students responded to questions in the same manner in which they
were asked. The same coding also allowed this researcher to calculate correct responses and
matches by adding an additional coding tier for hits and misses. This did get conflated when
teachers ask a question where they wanted an open answer, but they worded the question in a
way that limited students’ responses to a yes or no. Overall, this researcher examined question
types and student response types based on teachers’ intent for the students’ responses and
whether or not the students’ responses reflected the teachers’ question type.
Student responses to questions posed by the teacher were examined to see if the students’
response matched the type of question the teacher posed. For example, if the teacher asked a
literal question and the student provided a literal answer, then, they were both coded as literal.
But, if the teacher asked a literal question and the student answered with an inference response,
then, the teacher question and the student response were coded differently and did not match. In
order to address incorrect answers and responses that did not match the question type, an
additional tier was created in ELAN (Max Planck, 2012) in order to record matches, mismatches,
and incorrect answers for student responses in order to look at whether or not the student
responses were reflecting the teacher questions. If a response matched a question, and was
correct, it was coded as a “match.” Student responses matched teacher questions 91% of the
time. If the response matched the question type but was wrong, it was coded as “incorrect.”
READ-ALOUD INTERACTIONS
17
Student responses were incorrect 7% of the time. If a response did not match the question type
asked, it was coded as a “mismatch,” which only occurred 2% of the overall responses.
There were occasions when students answered a question incorrectly and teachers had
two distinct ways of dealing with incorrect answers. First, they simply moved on to another
student to respond without acknowledging the response as right or wrong. For example, when
Mrs. Smith (event two) was reading Mishe-Mokwa and the Legend of Sleeping Bear (Sproul,
1979) and asked students to identify which state the bears were in before they started swimming.
A student responded “Michigan” when the correct response should have been Wisconsin. Mrs.
Smith simply called on another student who replied “Wisconsin” and the teacher affirmed her
answer as correct. Secondly, teachers identified that the response was wrong and moved on to
another student to answer the question. Durkin (1978-1979) also recognized this issue, “Rarely,
for example, was anything done with wrong answers except to say that they were wrong. Never
did children have to prove or show why they thought an answer was correct. Often times,
students were not asked to explain correct answers either. Frequently, in fact, the emphasis
seemed to be on guessing what the teacher’s answer was rather than on recalling what had been
read” (490). Often times, students were not asked to explain correct answers either. Overall,
there were 21 incorrect responses and no teacher asked for a student to explain or justify his or
her incorrect answers.
The third question, in regard to the relationship between the level of teachers’ questions
and the allocation of student talk time, was answered by looking across categories in order to
determine if there was a relationship. This researcher took a step back and examined the types of
questions teachers asked and how much time was allocated to students for talking about the text
and answering those questions. The answers to these questions were determined by what
READ-ALOUD INTERACTIONS
18
actually occurred in the data. Teacher questions types were examined with the total amount of
talk time allocated to students in order to see if any pattern emerged.
In order to examine this question, this researcher calculated the amount of time allocated
to each actor—teacher, students (combined as a single actor), text, and silence—during each
event. An annotation tier in ELAN (Max Planck, 2012) was used to code when the teacher was
talking, the text was being read, students were talking, and periods of silence that lasted five
seconds or more. Table 4 lists the actor and the amount of time in minutes and percentages
allocated to each classroom.
Table 4 Total Talk Time by Minutes and Percentages
Teac
her
Perc
enta
ge o
f Te
ache
r Tal
k Ti
me
Text
Perc
enta
ge o
f Te
xt T
alk
Tim
e
Stud
ents
Perc
enta
ge o
f St
uden
t Tal
k Ti
me
Sile
nce
Perc
enta
ge o
f Si
lenc
e
Tota
ls
Brown 4.70 9% 43.22 84% 3.62 7% 0.00 0% 51.54
Davis 20.70 37% 23.95 43% 10.47 19% 0.22 0% 55.34
Johnson 17.85 32% 30.17 53% 6.97 12% 1.62 3% 56.61
Jones 37.80 47% 28.35 35% 14.13 18% 0.12 0% 80.40
Smith 11.52 37% 15.40 49% 4.40 14% 0.23 1% 31.55
Williams 8.05 35% 13.55 58% 1.13 5% 0.45 2% 23.18
Totals 100.62 34% 154.64 52% 40.72 14% 2.64 1% 298.62
The total amount of time allocated to students was less than half of teacher talk time.
Individually, the teachers varied from 5-19%. These percentages were, then, used to look at the
kinds of questions teachers asked to see if there was any type of relationship. Teachers varied in
the amount of questions asked and which types they used the most, as shown in Table 5.
READ-ALOUD INTERACTIONS
19
Management questions are included this time in order to see the proportion of time teachers were
dealing with management issues as opposed to asking questions about the text.
Table 5 Total Questions by Type and Teacher
Brown Davis Johnson Jones Smith Williams Total
inference 9 30 11 29 3 2 84 literal 2 7 8 12 13 3 45 management 4 13 8 32 35 9 101 personal response 0 18 4 10 0 4 36 prediction 6 8 0 1 1 0 16 prior knowledge 5 26 10 32 4 3 80 vocabulary 0 4 0 6 1 3 14 Totals 26 106 41 122 57 24 376
My expectation was that if a teacher was asking higher level questions, then the amount
of talk time allocated to students would be a greater percentage than those asking lower level
questions. This turned out to be false. The issue of open and closed questions impacted the
amount of student talk time, therefore, the results varied and no relationship was apparent.
Overall, the lowest and the highest percentages of student talk time were Mrs. Williams and Mrs.
Davis. The data from these two teachers matched what this researcher expected to see, however,
the four teachers in the middle did not meet the expectation. Therefore, no relationship between
student talk time and the level of teachers’ questions could be determined using this data.
The Management of the Read-aloud
Each of the individual events in this study added up to one big story related to the
management of the read-aloud. Overall, in each read-aloud, teachers were in control and worked
READ-ALOUD INTERACTIONS
20
constantly to maintain control during each read-aloud event. The general pattern of talk for each
teacher began with a teacher question, led to a student response; the teacher then evaluated the
response and moved on asking another student to respond or returning to the text. On the whole,
teachers did not display knowledge of asking effective questions.
Prior research clearly recommended that teachers ask higher-level questions. The data
revealed that it was likely that teachers were aware of this. Overall, 31% of the questions asked
were inference and only 16% were literal. This suggests that the teachers are aware they should
be asking students to put together information from the text and that they should be able to draw
conclusions from the reading. The paradox lies in the fact that even though the teachers were
asking inferential questions 31% of the time, 64% of those questions were closed in nature.
Overall, teachers asked closed questions 77% of the time, which means that 77% of the time
students were able to answer a question with yes, no, or one word. This did not enable the
student to display their knowledge about the text and limited their thinking. Consider the
following exchange from Mrs. Davis (event three):
Mrs. Davis: Do you think these are kids that you do or do not want to run into?
Multiple Students: Do not
When Mrs. Davis began the question with “do you think,” she automatically limited the answer
to two choices. Additionally, she lead them to the answer of “do not” when she asked the
question and emphasized the “do not” in her question with her tone of voice. Essentially, this
wording closed off the questions and left little time for students to talk and kept the teacher in
control of the conversation and the thinking. Perhaps it would have been a better conversation if
she had asked, “What do you think about these kids that have appeared in the woods?” With the
READ-ALOUD INTERACTIONS
21
question worded this way, it is up to the student to make the inference and they will have the
opportunity to express their own thoughts rather than just confirming the teacher’s thoughts.
By posing closed questions, teachers in this study failed to assist students as much as
could be achieved through open-ended questions. The data suggested that teachers asked fewer
literal questions than in previous studies, however, the prevalence of higher level-intent
questions posed in a closed manner indicates that teacher have not mastered the fine art of
questioning. When this happened, the teachers lost opportunities to engage students in quality
conversations about the text being read.
One possible reason for this is simply a matter of time. There are only so many minutes
in a day and teachers have to move through one lesson to get to the next one. A read-aloud is a
conscious choice that also must be balanced with other forms of instruction. It is probable that
the teachers are simply posing questions in order to present the “right” answers and move on to
the next lesson. The blame for this only partially falls on the teacher; it is also an unfortunate
outcome of the increased demands on teacher-time due to the pressures of high-stakes testing.
Another possible reason for the lack of effective questions may be lack of teacher
reflection on questioning or insufficient training in questioning techniques. In my experience,
pre-service courses discuss questioning, however, effective questioning may be one issue that
falls to the wayside by the time the pre-service teacher is in a classroom with the ever-increasing
demands of teaching and test preparation. Many undergraduates may not always know what is
and is not pertinent from courses and how to apply it once they are teaching full time. Also,
teacher manuals provide the specific question rather than suggestions and instruction for how to
ask questions. The possibility exists that teachers have not been trained in the “how” of
questioning.
READ-ALOUD INTERACTIONS
22
In the participant classrooms, however, questioning appears to be a means of controlling
the talk and managing student participation during a read-aloud. As the results suggested,
questioning may have become a means to control the talk and manage the classroom rather than
having deep and meaningful discussions about text.
Implications for Teaching
First and foremost, teachers need to determine why they are asking students questions
during instructional read-alouds. There are a multitude of reasons from checking understanding,
looking for misconceptions, or assessment. Then, teachers can determine what they need to
change based on the purpose of their questions. “Changing our talk requires gaining a sense of
what we are doing, our options, their consequences, and why we make the choices we make”
(Johnston, 2012, p. 7). This study focused on the questions that teachers asked in order to elicit
conversation from students during a read-aloud. Other factors were examined, such as student
responses and the allocation of talk time. As a result of these data, this study has one major
implication: Teachers need to be explicit when planning the questions they ask during read-
alouds.
I saw no evidence, in my observations or in the post-observation surveys filled out by
each teacher, of questions that were planned prior to the read-aloud. Teachers need to plan out
questions to ask students when reading aloud by pre-reading the text and thinking about the
possibilities for discussion. Questions can be written on sticky notes and tagged in the book as
further modeling for students of how to interact with text. By planning out questions, teachers
will be able to word them in ways that ensure a student is the one doing the work in order to
answer higher-level questions and show what they are thinking. This kind of thinking needs to
READ-ALOUD INTERACTIONS
23
be intentional to “…choose our words and, in the process, construct the classroom worlds for our
students and ourselves” (Johnston, 2012, p. 1). By preparing for read-alouds intentionally,
teachers will be modeling for students how to ask open questions and they can, then, further this
thinking by having students write open questions about the read-aloud.
Teachers could benefit from being videotaped and using ELAN to ascertain what
questions they are actually asking and how they can ask better questions to encourage student
input. Video recording can be done simply and inexpensively by using smart phones and
laptops. Once a recording is done, examine it to see what kinds of questions are being asked and
how much of the talk time is being taken up by students or teacher. Teachers can figure out what
needs improvement, then, specifically plan for another read-aloud. The next step would be to
video record the planned read-aloud and revisit the questions above in order to determine the
improvements that have taken place. One final note, do not be intimidated by ELAN; it is free
and user friendly. It is helpful when educators enlist the assistance of colleagues to work with
and help each other with the recordings, analysis, and making positive impacts on teaching.
Limitations of the Study
There were three overall limitations to this study. This study was conducted in one
geographical location and only analyzed two events for each of six elementary teachers. The
data collected and conclusions represented one set of participants and, therefore, were not
generalizable to the general population. There was also a difference in the read-aloud time spent
in each classroom. The shortest read-aloud was just under ten minutes and the longest was
almost 43 minutes. This also limited the conclusions that could be drawn. Lastly, the texts were
teacher-selected and resulted in a variety of formats and genres. The read-alouds were for
READ-ALOUD INTERACTIONS
24
instructional purposes, which resulted in reading during a variety of content-area blocks. This
resulted in different read-aloud styles and instructional focus, which also limited conclusions to
this population of teachers.
Conclusion
The overall conclusion of this study is that teachers need to give up some of the control
during an instructional read-aloud experience. Control can be shared with students by being
more explicit and planning for questions to ask during read-alouds in order to allow students to
show their thinking and discuss the text being read. Specific training using a framework for talk,
particularly the work of Johnston (2004) would be beneficial for teachers’ construction of
questions, and, in turn, increase the amount of talk time allocated to students during an
instructional read-aloud. By using the framework, teachers will be led to preplan effective
questions that can be asked during an instructional read-aloud in order to engage students in
meaningful learning about text.
READ-ALOUD INTERACTIONS
25
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ProQuest Dissertations & Theses A&I, 3484783.
1
An Analysis of School Climate and Student Growth in Select Michigan Charter Schools
Benjamin P. Jankens, Ed.D.
Central Michigan University
Biography
Dr. Benjamin P. Jankens is an Assistant Professor and program director in the Department of
Educational Leadership at Central Michigan University. He earned his doctor of educational
leadership from Eastern Michigan University. Prior to his work as an professor, Dr. Jankens
served in various charter school roles, including teacher, principal, superintendent, and Assistant
Director for the Governor John Engler Center for Charter Schools. His research interests cover a
broad array of educational reform and policy issues in the K-12 setting, including: the school
environment and its effects on student performance; school performance evaluation systems; and
school reform efforts, including charter school and authorizing practices.
2
Abstract
The purpose of this study was to examine the relationship between school climate and student
growth in Michigan charter schools. The Organizational Climate Descriptive Questionnaire for
Elementary Schools (OCDQ-RE) developed by Hoy, Tarter and Kottkamp (1991) was used to
assess teacher perceptions of school climate. Student growth data were calculated using the fall
and spring reading and math results from the Performance Series test by Scantron, and the MAP
test by NWEA. A non-experimental, quantitative analysis was used to test for variables in
participating charter elementary schools in Michigan. The results indicated there is a significant
relationship between school climate and student growth in the select schools and reinforce that
the school environment is a key variable in student performance. Understanding the variables
that affect a student’s performance, both academically and social, will provide insight for school
leaders to help focus on factors that contribute to increased student performance. While
educators and researchers have been studying the dynamics within the school environment and
how they impact student outcomes for many years, this study departs from the traditional student
achievement variable and instead uses student growth to determine what influence the school
climate may have on student performance. Additionally, this study looks at the new environment
of charter public schools.
Keywords: School Climate, School Environment, School Outcomes, Student Growth, Charter
Schools
NOTE: This research paper is an adaption of work conducted for a doctoral dissertation in
2010-2011.
3
Introduction
Due to the countless social dynamics students and schools face on a daily basis, the
environment surrounding student learning and the factors that have a direct or indirect impact on
student outcomes is as broad and elusive as ever (Cohen et al., 2009). School climate is a
complex, multidimensional phenomenon which influences many aspects of the school and
greater community in which it resides (Marshall, 2004). Although extensive research
demonstrates school climate contributes to a student’s overall well-being (Cohen &
D’Alessandro, 2013), the factors that contribute to school climate is less understood (Anderson,
1982; Marshal, 2004). Despite a wealth of empirical data on student achievement and factors
related to student outcomes, teachers are still uncertain of their impact on students (Fullan, 2007).
Research has demonstrated that there are many variables that contribute to influencing a
student’s academic success, which include: instructional strategies, classroom resources, school
culture or current school climate, as well as the student’s socioeconomic status, previous
educational attainment and his or her past educational experiences (Brookover et al., 1978).
Although the use of student achievement as a measure of school success is still debated (Boyles,
2000), a significant body of empirical research demonstrates that various characteristics of
school climate influence student outcomes (Brookover et al., 1978; Freiberg, 1999; Hoy &
Clover, 1986; Hoy et al., 1991). The variables used in this research, however, have primarily
focused on student achievement (Brookover et al., 1978; Hoy & Clover, 1986; Hoy, 1997; Kelly,
2005). Rarely, however, has student growth in reading and math been a topic of conversation in
relationship to school climate.
The purpose of this quantitative study was to investigate the relationship between school
climate and student growth within a selected cohort of Michigan public charter schools. Not only
4
is student growth a new approach to understanding school climate, the overall charter school
environment is one that has yet to be fully understood. To examine the relationship between
these two variables, participating charter schools in Michigan with a common authorizer were
surveyed using the Organizational Climate Descriptive Questionnaire for Elementary Schools
(OCDQ-RE), a school climate inventory (Hoy, Tarter, & Kottkamp, 1991). This survey
examined aspects of school climate, including teacher perceptions of teacher and leader
behaviors, as well as other variables surrounding the learning environment.
To complete this quantitative research study, we investigated the following questions
1. What is the relationship between school climate, as measured by the OCDQ-RE, and
student gain scores in reading and math, as measured by the Performance Series or MAP
tests?
2. What are the common characteristics of schools with similar climate results, as measured
by the OCDQ-RE?
The grounds for comparison focused on the variables associated with school climate in a
select group of charter schools in Michigan. School climate data served as the independent
variable and was gathered using the OCDQ-RE survey by Hoy et al. (1991). The dependent
variable, student growth, was measured using both fall and spring scaled scores from the same
academic year, from Scantron’s Performance Series and Northwest Evaluation Association’s
Measures of Student Progress’s (MAP) reading and math tests administered in grades 3 through
8. From these raw scaled scores, each student’s gain score was then compared to the student’s
expected gain based on national results to create a percentage of normal growth.
School Climate
Unlike school culture, which is rooted in the field of sociology and anthropology, school
5
climate is an adaption of organizational climate (Lewin, Lippit, & White, 1939) which seeks to
apply business concepts to schools. The concept of school climate, as a specific component of
the learning environment, was originally drawn from organizational theory research from the
mid-1900s (Anderson, 1982; Cohen, McCabe, Michelli, & Pickeral, 2009; Perry, 1908). The
1950s brought the dawn of organizational climate with researchers such as Pace and Stern
(1958), March and Simon (1958) and Halpin and Croft (1963) first defining organizational
climate as “organizational life” or “work environment.” It was Argyris (1958) who introduced
organizational climate as a concept and provided a comprehensive definition of the term that is
still used today; defining climate as the formal policies, employee needs, values, and
personalities of the organization. Because of its all-encompassing scope, this definition
contributed to the ambivalent relationship between climate and culture that continued over the
next twenty years (Kundu, 2007).
Although many explanations of organizational and school climate have been used over
the past century, there is still no commonly used definition (Cohen, 2006). Originally developed
by Tagiuri (1968), organizational climate can be broken down into four main distinctions:
ecology, milieu, social system, and culture. Although Tagiuri’s definition serves as a foundation
for identifying the main components of organizational climate, Moos (1974) and Insel and Moos
(1974) added that organizational climate can be separated into two additional dimensions:
physical and social. Additional work in the area of organizational climate was further refined by
research in, which provides the following characteristics of school climate:
Peoples’ shared perceptions of the school or department (Freiberg, 1999; Hoy, Tarter &
Kottkamp, 1991; Stolp & Smith, 1995),
The collective impressions, feelings, and expectations of individuals within a school
6
(Freiberg, 1999; Stolp & Smith, 1995),
Perceptions of the school’s structure and setting (Freiberg, 1999; Stolp & Smith, 1995),
Social interactions and behaviors among individuals who work or spend time in the
school (Stolp & Smith, 1995; Hoy, Tarter, & Kottkamp, 1991; Freiberg, 1999),
Something that is immediate and present, not historical (Freiberg, 1999; Stolp & Smith,
1995), and
Something that surrounds us and is influenced by us, but is not integral or part of us
(Stolp & Smith, 1995).
Using this foundation, the following definition of school climate is presented for the
purpose of this study, capturing the essence of an organization’s character (Hoy & Miskel, 1987;
Hoy, Tarter & Kottkamp, 1991; Tagiuri, 1968):
The relatively enduring quality of the school environment that is experienced by
participants, affects their behaviors, and is based on their collective perceptions of
behaviors in schools.
Student Growth
In the current high-stakes environment of public education, schools are becoming more
accustomed to high levels of accountability. Most of the state-mandated assessments are only
proctored once a year and provide limited data in regard to student performance. Primarily
measuring static achievement, these single-use tests do not provide rich information into the
complex makeup of a school or classroom, such as variables between academic years, teachers,
programs, school culture and climate, or even different schools themselves.
In order to gain insight into student performance relevant to a specific timeframe,
program, or instructor, a more flexible assessment model – one that seeks to measure student
7
gains – is needed. Computer adaptive testing systems, such as the Performance Series and MAP
tests, provide data that are accurate and reliable. Not only are students assessed on material from
their grade level, but the test also adapts to their achievement level. By using multiple test
windows within a school year, these computer adaptive tests measure both student achievement
and growth. As student growth becomes a more accessible evaluation tool, more educational
leaders are looking to implement such assessments (Gong, 2004). Using student growth over a
set period of time provides a method by which to measure the effects variables, such as climate,
have on learning (Betebenner, 2008).
Charter Schools
It’s now widely accepted that the American school system has its shortcomings and needs
to improve, but the approach to improvement is still up for debate (Weil, 2000). Despite the vast
variations of charter legislation, chartering process, oversight and operations, the one
commonality of charter schools across the country is “individuality.” A charter school by its
very nature is created to do things differently than the traditional district schools it neighbors
(Nathan, 1996). Although charter schools are not that dissimilar from their traditional
counterparts, their entire existence is to innovate and span a void that the one-size-fits-all
districts struggle to fill.
The characteristics of charter schools range as widely as the communities in which they
reside. From a fine art integrated curriculum to a college-prep program, or trade school, each
charter school seeks to find their own niche. A popular theme of charter schools across the
county is focusing on cultural identity, with the school’s focus being a reflection of that
particular community (Price & Jankens, 2015). Unfortunately, there is a lack of research
providing information on school climate itself, for charter schools in particular (Jankens, 2011).
8
Although the focus of this study is based on the relationship between school climate and
student growth in charter schools, institutional factors including class size, the competency of the
teachers, availability of learning resources, faculty workload, and overall program effectiveness
may also affect student outcomes (Ramsden & Entwistle, 1981). Ewell (1995) contends that
student services provided by the school, including special education, as well as a student’s
enrollment status and the location of the school all play a role in influencing student outcomes.
Therefore, providing additional empirical findings in the area of school climate and student
growth will contribute to a better understanding of our current education landscape and the
factors that contribute to student learning in charter public schools.
Conceptual Framework
The conceptual framework used in this study to examine the relationship between school
climate and student growth in selected Michigan charter schools rested on the Anderson (1982)
causal model constructed from Tagiuri’s (1968) taxonomy, which provided a comprehensive
assessment of the social environment. Using Anderson’s (1982) model as an additional point of
reference, Hoy’s (1986) six dimensions of the OCDQ-RE can be exchanged with Tagiuri’s four
dimensions of the organizational environment, focusing on teacher and principal perceptions
(Figure 1). The principal’s behavior, made up of three dimensions (supportive, directive,
restrictive), interacts with the teachers’ collective behavior, made up of three dimensions
(collegial, intimate, disengaged), which in turn interacts with and establishes the school climate.
The three distinct behaviors for both the principal and the teachers overlap to form two
openness dimensions: principal openness and teacher openness. Figure 1 conceptualizes all
possible interactions between the teachers and principal in the school environment as they relate
to one another and formulate openness, and the collective school climate.
9
Figure 2 builds upon the interrelationships among Hoy’s six climate dimensions and
illustrates the interactions between the collective school climate and student outcomes. Similar
to that in Anderson’s model (1982), the outcomes have an interaction with the collective school
climate. Outcome1 is shown as affecting both school climate and Outcome2, however,
outcome1 is not directly affected by outcome2 as time is a factor in this relationship. For the
purpose of measuring student growth within an elementary school, two points of reference are
being used to create a growth score: Outcome1 (the results of a pretest), and Outcome2 (the
results of a posttest). Therefore, outcome1 may have an interaction effect on outcome2, but
outcome2 cannot have a reciprocal relationship with outcome1 (Figure 2). This is divergent from
Anderson (1982), who measured student achievement at a single point in time using a status
score (Anderson, 1982).
10
The conceptual framework used to illustrate the theory of school climate and its
relationship to student growth within this study combine the concept of school climate developed
through the OCDQ-RE by Hoy et al. (1991) with that of student growth. Using the OCDQ-RE to
measure climate and student growth as a measure of student outcomes, this new approach is a
progression of Anderson’s (1982) work seeking to understand how school climate affects student
achievement (Figure 1 & 2).
Research Design
In order to explore the issues surrounding school climate and student outcomes, a non-
experimental quantitative study was used to investigate these relationships within select
Michigan charter schools. In order to collect quantitative data for this study, researchers used the
OCDQ-RE. As pioneers in the field of organizational climate in schools, Halpin and Croft
designed the first Organizational Climate Descriptive Questionnaire (OCDQ) in 1963, providing
11
the field a reliable instrument with which to gather data about school climate. Hoy, Tarter, and
Kottkamp (1991) provided additional groundwork of empirical research through reliable and
valid measures with their work on school climate and the OCDQ-RE in Open Schools/Healthy
Schools (1991). Through many years of testing and refining, they have garnered support and
provided the field of educational research with a solid framework within which to work, aimed
toward organizational improvement (Cohen et al., 2009; Hoy et al., 1991).
Each survey was comprised of 42 questions and scored using a four-point Likert-type
scale (1 = Rarely Occurs, 2 = Sometimes Occurs, 3 = Often Occurs, 4 = Very Frequently
Occurs). The survey responses sought to provide six dimensions of responses separated into two
categories: principal behavior and teacher behavior.
Researchers obtained student growth data from the public state university authorizer who
chartered the participating schools. Student test scores from 2010-2011 were gathered from
either the Performance Series test by Scantron or the MAP by Northwest Evaluation Association
(NWEA); scores originated from the reading and math tests administered in grades 3 through 8,
in both fall and spring semesters, depending on the assessments identified in each school’s
charter contract. Results were coded using a non-student-identifiable code in compliance with
the Family Educational Rights Privacy Act (FERPA), and a percent of normal growth was
calculated based on national results.
This study was limited by the data set available for the years gathered, as well as the time
the tests were administered (spring and fall). Additional delimitations were set, restricting the
perception of additional stakeholders to compose a full review of school climate; namely
administrators, students, parents and the community. Therefore, the school climate indexes only
present the view from the perspective of the teacher, as the individuals who are most closely
12
impacted by the leadership style of the school administration.
Population and Selecting Participants
The population for this study consisted of elementary charter schools in Michigan.
Because Michigan-based charter schools are authorized by one of many state universities,
community colleges, local intermediate school districts, or local school districts, the non-
probability sample was intended to limit the variables between charter schools with various
authorizing agencies. This limit sought to ensure the schools administered a common, nationally-
normed, criterion-referenced assessment – the Performance Series and the MAP tests – that were
used in the study to collect student assessment data.
The sample in this study was selected from schools chartered by the same public state
university authorizer within Michigan. Teachers and teacher assistants (or paraprofessionals)
from the schools were invited to participate in the study encompassing a total population of 355
teachers and paraprofessionals. Of the original 35 schools that were invited to take part in the
study, 11 participated (31%). Of the 266 surveys distributed, 224 were returned (84.2%). Data
collection began in February 2011 and ended in June 2011. School climate data was collected
through the use of a school climate survey. Student growth data was collected from the university
authorizer that chartered the schools participating in this study. The OCDQ-RE was used to
assess teacher’s perceptions of principals’ and fellow teachers’ behavior.
Data Analysis Procedures
Survey results from the OCDQ-RE were scored for each respondent, and teachers
perceptions were mapped to six dimensions of school climate – three for principal openness and
three for teacher openness. Each dimension is attributed to a particular characteristic of both the
principal and the collective group of teachers:
13
Supportive Principal Behavior;
Directive Principal Behavior;
Restrictive Principal Behavior;
Collegial Teacher Behavior;
Intimate Teacher Behavior; and
Disengaged Teacher Behavior.
Principal openness and teacher openness scores were also used to calculate the overall
openness scores of each school. An open climate was defined as having both teacher and
principal openness mean scores above 500. A closed climate existed when both teacher and
principal openness mean scores were below 500. An engaged climate was defined as having a
principal openness mean score below 500, with a teacher openness mean score above 500, and
the disengaged climate had a principal openness mean score above 500 and a teacher openness
mean score below 500.
Based on these calculations, openness scores were calculated for each school, as well as
for the principal and teachers. The characteristics of an open climate were cooperation, respect
and openness; all attributes that exist within the school environment, among the faculty and
between the faculty and principal (Hoy, 1991). Additionally, the principal within an open school
listens and is receptive to feedback, and provides frequent and genuine praise.
Student growth data was obtained from the public state university authorizer that
chartered the schools who participated in the study. Student test scores from 2010-2011 were
gathered from either the Performance Series or MAP tests. Scores originated from the reading
and math tests administered in grades 3 through 8, in both fall and spring semesters, depending
on the assessments identified in each school’s charter contract.
14
Data was analyzed using the Pearson Product-Moment, and the correlation coefficient
was used to compare the OCEQ scores of the schools with reading and math growth scores. In
addition, a linear regression analysis was conducted to determine what relationship exists
between school climate and student growth, and between schools with similar characteristics.
Findings
The OCDQ results were scored and analyzed describing the responses of 355 teachers,
categorized by the six standardized climate profiles, including the climate indices for principal
openness and teacher openness. The student growth scores for each school were obtained and a
percent of normal growth was calculated using national results. The percent of normal growth
Table 1. School Climate and Student Growth Comparison
School Perceived
Principal
Profile
Perceived
Teacher
Profile
Perceived
School
Climate
Mean % of
Normal
Growth
(Math)
Mean % of
Normal Growth
(Reading)
1 High
(572)
High
(588)
Open 1.62 1.85
2 Low
(426)
Below Average
(476)
Closed 0.98 1.21
3 Above Average
(534)
Above Average
(542)
Open 1.05 1.69
4 Average
(490)
Slightly Below
(476)
Closed 0.84 0.99
5 Above Average
(530)
High
(570)
Open 1.22 1.19
6 Average
(507)
Above Average
(526)
Open 1.01 1.37
7 Slightly Below
(479)
Below Average
(460)
Closed 0.54 0.61
8 High
(582)
High
(578)
Open 1.46 2.01
9 Very High
(665)
Slightly Above
(521)
Open 0.85 0.82
10 Very High
(630)
High
(552)
Open 0.67 0.85
11 Average
(507)
Average
(501)
Open 0.94 1.44
15
from the 11 schools who participated in the study is presented in Table 3 (n = 4015). Included in
the table are the minimum, maximum, mean and standard deviation. Additionally, the perceived
principal and teacher profiles for each school, along with the perceive school culture, were
compared to the mean of the students’ percent of normal growth for both math and reading
(Table 1). All schools posted perceptions of either open or closed school climates.
Using the Pearson r to test the relationship between the independent variable (school
climate) and the dependent variable results (student gain scores in reading and math) the results
indicated small to moderate, positive correlations between school climate and student growth
when analyzing math and Supportive Behavior (S), r = .18, p < 0.01; math growth and Collegial
Behavior (C), r = .20, p < 0.01; and math and Intimate Behavior (Int), r = .20, p < 0.01 (Table 2).
Conversely, there was a relatively small to moderate negative correlation between math and
Disengaged Behavior (Dis), r = -.20, p < 0.01, and a small negative correlation between math and
Table 2. Intercorrelations, Means and Standard Deviations for Climate Levels and Math Growth (N =
4015)
Variable 1 2 3 4 5 6 7 M SD
___________________________________________________________________________
1. Math Growth -- .18** -.08** .00 .20** .20** -.20** 1.02 1.04
2. Behavior (S) -- -- -.10** -.24** .64** .63** -.45** 581.11 68.37
3. Behavior (D) -- -- -- .50** -.37** -.15** .46** 537.18 61.18
4. Behavior (R) -- -- -- -- -.51** -.20** .21** 466.36 73.64
5. Behavior (C) -- -- -- -- -- .62** -.48** 536.67 50.51
6. Behavior (Int) -- -- -- -- -- -- -.15** 546.22 55.25
7. Behavior (Dis) -- -- -- -- -- -- -- 504.49 62.46
___________________________________________________________________________
**p < 0.01 (2-tailed)
16
Directive Behavior (D), r = -.08, p < 0.01.There was also a relatively weak positive correlation
between math and principal openness, r = .12, p < 0.01, and between math and teacher openness,
r = .26, p < 0.01 (Table 3).
Table 3. Correlation Between Principal and Teacher Openness and Math Growth (N=3993)
Variable 1 2 3 M SD
___________________________________________________________________________
1. Math Growth -- .12** .26** 1.02 1.039
2. Principal Openness -- -- .71** 525.85 48.946
3. Teacher Openness -- -- -- 526.13 45.503
___________________________________________________________________________
**p < 0.01 (2-tailed)
Reading results were analyzed, and of the 21 pairs of variables, 10 were significantly
correlated. There were weak positive correlations between reading growth and Supportive
Behavior (S), r = .09, p < 0.01; between reading and Collegial Behavior (C), r = .10, p < 0.01;
Table 4. Intercorrelations, Means and Standard Deviations for Climate and Reading Growth (N = 4015)
Variable 1 2 3 4 5 6 7 M SD
___________________________________________________________________________
1. Reading -- .09** -.05** .00 .10** .12** -.12** 1.27 2.07
2. Behavior (S) -- -- -.10** -.24** .64** .63** -.45** 581.1 68.37
3. Behavior (D) -- -- -- .50** -.37** -.15** .46** 537.18 61.18
4. Behavior (R) -- -- -- -- -.51** -.20** .21** 466.36 73.64
5. Behavior (C) -- -- -- -- -- .62** -.48** 536.67 50.51
6. Behavior (Int) -- -- -- -- -- -- -.15** 546.22 55.25
7. Behavior (Dis) -- -- -- -- -- -- -- 504.49 62.46
___________________________________________________________________________
**p < 0.01 (2-tailed)
17
and between reading and Intimate Behavior (Int), r = .12, p < 0.01 (Table 4). Conversely, there
was a small negative correlation between reading and Disengaged Behavior (Dis), r = -.12, p <
0.01, and between math and Directive Behavior (D), r = -.05, p < 0.01.As with math, there was a
weak positive correlation between reading and principal openness, r = .06, p < 0.01, and the same
between math and teacher openness, r = .14, p < 0.01 (Table 5).
Table 5. Correlation Between Principal and Teacher Openness and Reading Growth (N=4015)
Variable 1 2 3 M SD
___________________________________________________________________________
1. Reading Growth -- .06** .14** 1.27 2.074
2. Principal Openness -- -- .71** 525.85 48.946
3. Teacher Openness -- -- -- 526.13 45.503
___________________________________________________________________________
**p < 0.01 (2-tailed)
In order to determine the common characteristics of schools with similar climate results,
schools were scored and categorized based on the standardized openness indexes presented in
Table 1. A total of 8 of the 11 schools had both principal and teacher open indexes above 500
and were categorized as open schools. The other three schools had both principal and teacher
openness indexes below 500 and were categorized as closed schools. None of the eleven schools
were categorized as either engaged or disengaged schools.
The descriptive statistics in Table 6 and Table 7 show mean math scores for schools with
open climates are higher than those of closed schools, with means ranging from 0.67 to 1.94
(Table 6), and 0.54 to 0.98 (Table 7) respectively.
18
Table 6. Open Schools Growth Comparison (math)
Standard
Variable N Minimum Maximum Mean Deviation
School 1 475 -3.19 9.50 1.62 1.439
School 3 477 -2.07 4.19 1.05 0.761
School 5 475 -1.93 4.58 1.22 1.019
School 6 560 -5.08 6.99 1.01 0.944
School 8 179 -1.14 4.47 1.46 0.914
School 9 103 -1.45 4.06 0.85 1.039
School 10 270 -1.68 3.04 0.67 0.779
School 11 357 -1.51 4.03 1.94 0.805
Table 7. Closed Schools Growth Comparison (math)
Standard
Variable N Minimum Maximum Mean Deviation
School 2 185 -0.88 3.70 0.98 0.729
School 4 336 -2.62 3.67 0.84 0.958
School 7 576 -4.36 3.75 0.54 1.012
Descriptive statistics presented in Table 8 and Table 9 determined that the mean reading
scores for schools with open climates generally had higher and also similar scores than those of
closed schools, with means ranging from 0.82 to 2.01 (Table 8), and 0.61 to 1.21 (Table 9)
respectively.
19
Table 8. Open Schools Growth Comparison (reading)
Standard
Variable N Minimum Maximum Mean Deviation
School 1 454 -9.26 10.00 1.85 2.324
School 3 448 -7.80 9.21 1.65 2.287
School 5 477 -6.28 5.30 1.19 1.521
School 6 553 -8.39 9.06 1.34 1.717
School 8 175 -8.47 8.06 2.01 1.819
School 9 101 -9.13 8.50 0.82 3.352
School 10 263 -10.00 9.62 0.85 2.307
School 11 353 -8.34 8.27 1.38 1.568
Table 9. Closed Schools Growth Comparison (reading)
Standard
Variable N Minimum Maximum Mean Deviation
School 2 188 -6.59 8.81 1.21 1.678
School 4 338 -8.47 8.63 0.99 2.318
School 7 561 -9.20 7.97 0.61 2.045
Next, multiple regression analyses were conducted to examine the relationship among six
profiles of school climate based on teacher perceptions. The combination of variables to predict
math growth from principal openness and teacher openness was statistically significant, F(4,
3989) = 983.28, p< 0.001) (Table 10). Note that high principal openness explains 50% of the
variance of student growth in math, with low principal openness as the constant (R2 = .50).
Average principal openness and high teacher openness were also moderately strong predictors of
math growth. Average teacher openness was a relatively low predictor of math growth with low
20
teacher openness as the constant.
Table 10. Regression Analysis: Principal and Teacher Climate Profiles and Math Growth
Predictor Variable Beta (β) p-value
___________________________________________________________________________
Principal Openness High .709 .000
Principal Openness Average .541 .000
Teacher Openness High .509 .000
Teacher Openness Average .330 .000
Note: Constant = Principal Profile 3 (low), Teacher Profile 3 (low)
The combination of variables to predict reading growth from the principal’s openness and
teacher openness was also statistically significant, F(4, 3907) = 369.84, p< 0.001) (Table 11).
Note that high teacher openness and average teacher openness explains 28% of the variance of
student growth in reading, with low teacher openness as the constant (R2 = .28). High principal
openness and average principal openness are also moderate predictors of reading growth.
Table 11. Regression Analysis: Principal and Teacher Climate Profiles and Reading Growth
Predictor Variable Beta (β) p-value
___________________________________________________________________________
Principal Profile 1 (high) .684 .000
Principal Profile 2 (average) .620 .000
Teacher Profile 1 (high) .796 .000
Teacher Profile 2 (average) .518 .000
Note: Constant = Principal Profile 3 (low), Teacher Profile 3 (low)
Using the openness index variable to predict math growth, the regression analysis
provided results that were statistically significant, F(1, 3992) = 3181.73, p< 0.001). The beta
coefficient is presented in Table 12. Note that school openness significantly predicts student
21
growth in math, with the closed school index as the constant. The adjusted R2 Value was .44.
This indicates that 44% of the variance in math growth was explained by the model. Based on
these results, schools with open climates are more likely to have high math growth results than
schools with closed climates. According to Cohen (1988) this is a large effect.
Table 12. Regression Analysis: Open Schools and Math Growth
Predictor Variable Beta (β) p-value
___________________________________________________________________________
School Index: Open 1.139 .000
Note: Constant = School Index: Closed
Using the same openness index variable to predict reading growth, the regression analysis
provided results that were also statistically significant F(1, 4014) = 261.58, p< 0.001). The beta
coefficient is presented in Table 13. Note that school openness moderately predicts student
growth in reading, with the closed school index as the constant. The adjusted R2 Value was .25.
This indicates that 25% of the variance in reading growth was explained by the model. Based on
these results, schools with open climates are more likely to have high reading growth results than
schools with closed climates.
Table 13. Regression Analysis: Open Schools and Reading Growth
Predictor Variable Beta (β) p-value
___________________________________________________________________________
School Index: Open 1.430 .000
Note: Constant = School Index: Closed
Discussion
While examining the relationship between the variables of school climate and student
growth, it can be concluded that there is significant statistical evidence to support the
relationship between school climate and student growth in the selected population. Based upon
this analysis, school climate is correlated to student outcomes. This conclusion is consistent with
22
earlier studies looking at the relationship between school climate and student outcomes, namely
student achievement (Brookover, 1978; Cohen, 2009; Hoy et al., 1991). The results of
correlation analyses and multiple regression analyses for both math and reading, with associated
p-value of <0.01, provided statistical evidence to support this conclusion.
Researchers, therefore, concluded that there is a significant relationship between school
climate, based on teacher perceptions, and student math and reading growth from fall to spring
testing periods within a school year. Furthermore, study findings showed that schools with
similar school climates produce similar student math and reading growth results, based on the
results of correlation analyses and multiple regression analyses for both math and reading, with
associated p-value of <0.01. These analyses provided statistical evidence to support this
conclusion.
The findings of this study in charter schools are similar to those of previous research in
traditional public schools. Providing additional support for the work of Brookover (1978) and
Hoy and colleagues (1986, 1991 & 2004), the study found that there is a relationship between
school climate and student outcomes. This result supports the notion that the school environment,
which is experienced by and affects the behaviors of its participants, plays a significant role in
the learning process of students.
Although this study adds further support to the already sound research on school climate
and its impact on student outcomes, it is the approach of using student growth within a school
year, as well as additional data on charter school climate that lends additional implication for
practice in the field of education. The design used in this study provides school leaders and
educational researchers with a reliable method to measure various factors that contribute to the
learning environment at a specific point in time. By using the pretest and posttest approach (with
23
fall and spring test results), this study provided a model that focuses the impact of the chosen
variables within a more controlled environment: a single school year. And, unlike student
achievement that is influenced by a multitude of factors, including students’ past academic
experiences, socioeconomic status and home life, student growth focuses on the amount of
academic progress a student has made within a set period of time, despite the students’ current
level of educational attainment.
Additionally, by using a lens that looked at the charter school environment as the unit of
analysis, this study provided additional clarification on the impact of school climate on student
learning within a charter school. As a relatively new field of study, much of the research on
charter schools either focuses on student achievement as the measure of student outcomes, or
overlooks the school environment as a factor in the school’s influence on student performance
(Hoxby & Rockoff, 2004; Miron & Nelson, 2002).
In summary, the body of literature and research regarding the relationship between school
climate and student achievement has a longstanding tradition (Anderson, 1982; Cohen et al.,
2009; Frieberg, 1999; Zullig, 2010). Although the association of student achievement to student
growth has been explored by way of year-to-year gain (Betebenner, 2008), more research is
needed and this study lends additional support to the focus on how school climates affect student
growth within a school year. Additionally, this study continues to provide support to the
importance of school climate on the social-emotional implications of a student’s well-being, let
alone the student’s academic success (Becker & Luthar, 2002; Haynes, Emmons & Ben-Avie,
2010). This study also provides charter school leaders with a better understanding of the
importance of school climate in the broader context of school activities, and the ability of
teachers and principals to influence the current school climate.
24
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